Monthly Archives: March 2016

I've worked on temporal ordering (particularly with smoking), but since creating visual timelines is a pretty reliable way of determining discrepancies by looking at them, I've spent the last couple of weeks looking into how best to approach solid data visualization. I'm going to start by brushing up on my JavaScript skills so that I can create interactive browser-based visualizations. They won't be live until I'm sure that generating live timelines (with no names or other identifying information, but even so) doesn't violate any privacy issues (or HIPAA of course).

Along the way, I'm working on code that will automatically detect discrepancies in the narratives -- that part is a little slower going, but that's why it's a long project. Onward!

This past week, I created more CSVs for mentions of CAD, hypertension, hyperlipidemia, and obesity. These CSVs are based on whether or not each condition is mentioned at all, so there are only two possible optionsĀ (mentioned or not mentioned). But in many cases, we have more information than that about the conditions or related events. Over this next week, which is spring break, I'll be looking into how to organizeĀ and analyze this more complex information.

Over the last couple of weeks, I extended a little bit of what I was working on (ordering temporal annotations with regard to other treatments). Although initially I used tuples (time, description) to track changes in medication/smoking, I recently switched to using a custom object approach (i.e., a medication object, smoking history object, etc) and then sorting them according to time (which can be extracted simply via function).

This upcoming week is Simmons' spring break, so I'm hoping to spend some extra time working on not only finding temporal relationships between medications/smoking, etc, but also on visualizations of these in a more concrete timeline fashion, so we don't have to make timelines manually.